#### MiningMath

Global Optimization with no stepwise process!

#### Understanding Blending and Sum Constraints

Open pit mine planning with blending constraints involves mixing ores from different sections of the mine to achieve a consistent grade and chemical composition. This ensures that the ore meets specific quality standards required for further processing, such as smelting or refining. Meanwhile, sum constraints regulate the total amount of material that can be mined, transported, or processed within a given timeframe. These constraints help in setting limits on the volume of ore and waste material handled, ensuring that operations remain within the capacity of available resources and infrastructure.

#### Integrating Blending and Sum Constraints

For a mine planner, the use of blending and sum constraints is crucial for optimizing the efficiency, profitability, and sustainability of mining operations. MiningMath integrates both constraints within its objective function, rather than applying them post-pit optimization.

By incorporating blending and sum constraints into the objective function from the outset, the MiningMath’s optimization process considers all relevant factors simultaneously. This leads to more accurate and efficient planning, as the system optimizes not only for maximum ore recovery and profit but also for quality control and production limits. Considering constraints early helps mine planners design operations that are not only profitable in the short term but also sustainable in the long term. It promotes the responsible use of resources and environmental management.

#### Modeling Blending and Sum Constraints with MiningMath

MiningMath allows users to include blending and sum constraints, and consequently, find solutions that are closer to real mining operations.

##### Blending (Average) Constraint

Blending can be included as an average constraint. This will control the average of any quantifiable parameter modeled block by block. Some other examples using average could be the haulage distance, based on the destination each block, or blasting material consumptions.

##### Sum Constraints

Sum constraints are based on the sum of any quantifiable parameter modeled block by block. Some examples include: tonnages and proportions of rock type and metal production;  consumption of inputs such as energy spent during comminution, and fleet hours spent to mobilize material; and contaminants control on the processing plant during each period.

#### Transform Your Open Pit Mine Planning with MiningMath's Blending and Sum Constraint

With MiningMath’s single-step, optimization engine, you can uncover opportunities that manual or stepwise planning might miss. Ultimately, this engine is able to optimize resource utilization and can improve project outcomes. Transform your mine planning process by leveraging blending (average) and sum constraints in the optimization process and take your mining projects to new heights of efficiency and success.

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Windows 64-Bit (x86_64) - 121 MB

Windows 64-Bit (x86_64) - 121 MB